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Found 2 papers, 1 papers with code
Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment
We propose to build the notion of continual learning (CL) into the modeling process of learning wireless systems, so that the learning model can incrementally adapt to the new episodes, {\it without forgetting} knowledge learned from the previous episodes.
Learning to Beamform in Heterogeneous Massive MIMO Networks
It is well-known that the problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks is challenging because of its non-convexity, and conventional optimization based algorithms suffer from high computational costs.
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